R/fdp_staplmer.R
fdp_staplmer.fit.RdFunctional Dirichlet Process Spatial Temporal Aggregated Predictor Linear Mixed Effects Regression Model Fit
fdp_staplmer.fit( y, Z, X, W, S, subj_mat, subj_n, weights = rep(1, length(y)), alpha_a = 1, alpha_b = 1, sigma_a = 1, sigma_b = 1, tau_a = 1, tau_b = 1, K = 5, iter_max, burn_in, thin = 1, fix_alpha = FALSE, bw = FALSE, seed = NULL )
| y | vector of outcomes |
|---|---|
| Z | design matrix |
| X | stap design matrix |
| W | group terms design matrix from |
| S | list of penalty matrices from |
| subj_mat | matrix indexing subject-measurement locations in (Z,X,W) |
| subj_n | vector of number of subject measurements |
| weights | weights for weighted regression - default is vector of ones |
| alpha_a | alpha gamma prior hyperparameter |
| alpha_b | alpha gamma prior hyperparameter |
| sigma_a | precision gamma prior hyperparameter |
| sigma_b | precision gamma prior hyperparameter |
| tau_a | penalty parameters gamma prior hyperparameter |
| tau_b | penalty parameters gamma prior hyperparameter |
| K | truncation number for DP mixture components |
| iter_max | maximum number of iterations |
| burn_in | number of iterations to burn-in |
| thin | number by which to thin samples |
| fix_alpha | boolean value |
| bw | boolean value indicating whether or not subject decomposition is used |
| seed | random number generator seed will be set to default value if not by user |